Partial Correlation Method Research Articles

An Investigation of the Relationship between Autonomous Learning and Lifelong Learning

The present study aims to investigate the relationship between autonomous learning and lifelong learning. The study group consists of 657 secondary school students enrolled in three public schools, aged 11 – 16 from Sakarya, Turkey. In this study, relationships between the variables of autonomous learning and lifelong learning and the sub-dimensions of autonomous learning were investigated. Pearson Product Moment Correlation, Partial Correlation methods were utilized in order to investigate these relationships. Autonomous Learning Scale (12 items) which was developed by Macaskill and Taylor (2010), was adapted to Turkish by Arslan and Yurdakul (2015) was conducted to measure autonomous learning. Lifelong Learning Scale (14 items) which was developed by Kirby, Knapper, Lamon and Egnatoff (2010), was adapted to Turkish by Arslan and Akcaalan (2015) was carried out to assess lifelong learning level. In the light of the research findings, it can be purported that autonomous learning and lifelong learning scores were correlating significantly. According to the existing results, it can be argued that autonomous learning which can be defined as the capacity to take charge of one’s own learning, has a positive interaction with lifelong learning which can be found voice in one’s ongoing, voluntary, and self-motivated learning.

Differentially correlated genes in co-expression networks control phenotype transitions

Background: Co-expression networks are a tool widely used for analysis of “Big Data” in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer). Methods: Co-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for two mus musculus datasets: B10.A strain (12 normal, 12 BcKO) and BALB/c strain (10 normal, 10 BcKO). Co-expression networks of cervical cancer (normal and cancer) were reconstructed using local partial correlation method for five datasets (total of 64 normal, 148 cancer). Differentially correlated pairs were identified along with the location of their genes in BcKO and in cancer networks. Minimum Shortest Path and Bi-partite Betweenness Centrality where statistically evaluated for differentially co-expressed genes in corresponding networks. Results: We show that in B cell deficiency the differentially co-expressed genes are highly enriched with immunoglobulin genes (causal genes). In cancer we found that differentially co-expressed genes act as “bottlenecks” rather than causal drivers with most flows that come from the key driver genes to the peripheral genes passing through differentially co-expressed genes. Using in vitro knockdown experiments for two out of 14 differentially co-expressed genes found in cervical cancer (FGFR2 and CACYBP), we showed that they play regulatory roles in cancer cell growth. Conclusion: Identifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype.

Graph reconstruction using covariance-based methods.

Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data.Electronic supplementary materialThe online version of this article (doi:10.1186/s13637-016-0052-y) contains supplementary material, which is available to authorized users.

Differentially correlated genes in co-expression networks control phenotype transitions.

Co-expression networks are a tool widely used for analysis of "Big Data" in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer). Co-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for two mus musculus datasets: B10.A strain (12 normal, 12 BcKO) and BALB/c strain (10 normal, 10 BcKO). Co-expression networks of cervical cancer (normal and cancer) were reconstructed using local partial correlation method for five datasets (total of 64 normal, 148 cancer). Differentially correlated pairs were identified along with the location of their genes in BcKO and in cancer networks. Minimum Shortest Path and Bi-partite Betweenness Centrality where statistically evaluated for differentially co-expressed genes in corresponding networks. Results: We show that in B cell deficiency the differentially co-expressed genes are highly enriched with immunoglobulin genes (causal genes). In cancer we found that differentially co-expressed genes act as "bottlenecks" rather than causal drivers with most flows that come from the key driver genes to the peripheral genes passing through differentially co-expressed genes. Using in vitro knockdown experiments for two out of 14 differentially co-expressed genes found in cervical cancer (FGFR2 and CACYBP), we showed that they play regulatory roles in cancer cell growth. Identifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype.

Rank Determination of Mental Functions by 1D Wavelets and Partial Correlation.

The main aim of this paper is to classify mental functions by the Wechsler Adult Intelligence Scale-Revised tests with a mixed method based on wavelets and partial correlation. The Wechsler Adult Intelligence Scale-Revised is a widely used test designed and applied for the classification of the adults cognitive skills in a comprehensive manner. In this paper, many different intellectual profiles have been taken into consideration to measure the relationship between the mental functioning and psychological disorder. We propose a method based on wavelets and correlation analysis for classifying mental functioning, by the analysis of some selected parameters measured by the Wechsler Adult Intelligence Scale-Revised tests. In particular, 1-D Continuous Wavelet Analysis, 1-D Wavelet Coefficient Method and Partial Correlation Method have been analyzed on some Wechsler Adult Intelligence Scale-Revised parameters such as School Education, Gender, Age, Performance Information Verbal and Full Scale Intelligence Quotient. In particular, we will show that gender variable has a negative but a significant role on age and Performance Information Verbal factors. The age parameters also has a significant relation in its role on Performance Information Verbal and Full Scale Intelligence Quotient change.

Investment Index Construction from Information Propagation Based on Transfer Entropy

In recent years, the number of individual investors continues to increase in financial markets. However, the large information gap between individual and institutional investors unduly impairs individual investors, which may negatively influence the market. In this study, we propose a new investment index that focuses on the relationships among stocks to help manage the risk of individual investors. The relationships among stocks have often been analyzed by a cross-correlation matrix method. However, such methods are strongly influenced by irregular events, including drawdowns. Therefore, we employed a transfer entropy method to analyze the relationships among stocks. Transfer entropy is a sequence analysis method proposed by Schreiber that is robust for irregular events. First, we applied the partial correlation and transfer entropy methods to test the data and confirm robustness for unexpected events. Next, we generated stock-networks by transfer entropy that represents the relationships among stocks. Finally, we proposed an investment index that is calculated from stock-networks which are generated from transfer entropy. We compared the proposed investment index with long positions and obtained higher performance investment by our proposed method than with a long position.

Factors of Local E-Government Development in Poland: The Case of the Poznań Agglomeration

Abstract This paper seeks to analyse factors of e-government development at the local government level in Poland. The analysis proceeded in three stages. In the first, a survey of the literature on the subject was made and a model of factors of e-government development was constructed. In the second, the factors distinguished were operationalised: indicators representing them were constructed. The third stage involved an empirical verification of the model using partial correlation and multiple regression methods; significant factors of e-government development were distinguished at the local government level. The analysis was conducted for a group of 18 communes making up the Poznań agglomeration. It was demonstrated that significant factors of local e-government development in Poland included the level of socio-economic development, inhabitants’ access to ICT, their attitudes and skills, the size of administrative units, attitudes of local authorities and leaders, a vision and a strategy of e-government development, human resources in offices, and the financial situation of a commune.

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods.

Investigation of The Relationships Between Lifelong Learning And Social Emotional Learning

The current research aims to investigate the relationship between lifelong learning and social emotional learning. Study group consists of 590 university students (390 females, 200 males) studying in different undergraduate programs in University of Sakarya, Faculty of Education in the Fall Term of 2015.In this research, relationships between the variables of lifelong learning and social emotional learning were investigated. Pearson Product Moment Correlation, Partial Correlation methods were utilized so as to investigate these relationships. As consequence of the research, lifelong learning and social emotional learning scores were correlating significantly. According to these results, it can be discussed that social emotional learning which can be explained as the behavioral reflection of social and emotional skills in a systematical and consistent way, has a positive relationship with lifelong learning which can be defined as all kinds of learning including formal or informal through one's life. Results are discussed within the context of the associated literature.

A novel joint sparse partial correlation method for estimating group functional networks

Advances in graph theory have provided a powerful tool to characterize brain networks. In particular, functional networks at group-level have great appeal to gain further insight into complex brain function, and to assess changes across disease conditions. These group networks, however, often have two main limitations. First, they are popularly estimated by directly averaging individual networks that are compromised by confounding variations. Secondly, functional networks have been estimated mainly through Pearson cross-correlation, without taking into account the influence of other regions. In this study, we propose a sparse group partial correlation method for robust estimation of functional networks based on a joint graphical models approach. To circumvent the issue of choosing the optimal regularization parameters, a stability selection method is employed to extract networks. The proposed method is, therefore, denoted as JGMSS. By applying JGMSS across simulated datasets, the resulting networks show consistently higher accuracy and sensitivity than those estimated using an alternative approach (the elastic-net regularization with stability selection, ENSS). The robustness of the JGMSS is evidenced by the independence of the estimated networks to choices of the initial set of regularization parameters. The performance of JGMSS in estimating group networks is further demonstrated with in vivo fMRI data (ASL and BOLD), which show that JGMSS can more robustly estimate brain hub regions at group-level and can better control intersubject variability than it is achieved using ENSS.

Left frontal glioma induces functional connectivity changes in syntax-related networks.

BackgroundA glioma leads to a global loss of functional connectivity among multiple regions. However, the relationships between performance/activation changes and functional connectivity remain unclear. Our previous studies (Brain 137:1193–1212; Brain Lang 110:71–80) have shown that a glioma in the left lateral premotor cortex or the opercular/triangular parts of the left inferior frontal gyrus causes agrammatic comprehension accompanied by abnormal activations in 14 syntax-related regions. We have also confirmed that a glioma in the other left frontal regions does not affect task performances and activation patterns.ResultsBy a partial correlation method for the time-series functional magnetic resonance imaging data, we analyzed the functional connectivity in 21 patients with a left frontal glioma. We observed that almost all of the functional connectivity exhibited chaotic changes in the agrammatic patients. In contrast, some functional connectivity was preserved in an orderly manner in the patients who showed normal performances and activation patterns. More specifically, these latter patients showed normal connectivity between the left fronto-parietal regions, as well as normal connectivity between the left triangular and orbital parts of the left inferior frontal gyrus.ConclusionsOur results indicate that these pathways are most crucial among the syntax-related networks. Both data from the activation patterns and functional connectivity, which are different in temporal domains, should thus be combined to assess any behavioral deficits associated with brain abnormalities.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-1104-6) contains supplementary material, which is available to authorized users.

Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins.

Motivation: Inferring structural dependencies among a protein’s side chains helps us understand their coupled motions. It is known that coupled fluctuations can reveal pathways of communication used for information propagation in a molecule. Side-chain conformations are commonly represented by multivariate angular variables, but existing partial correlation methods that can be applied to this inference task are not capable of handling multivariate angular data. We propose a novel method to infer direct couplings from this type of data, and show that this method is useful for identifying functional regions and their interactions in allosteric proteins.Results: We developed a novel extension of canonical correlation analysis (CCA), which we call ‘kernelized partial CCA’ (or simply KPCCA), and used it to infer direct couplings between side chains, while disentangling these couplings from indirect ones. Using the conformational information and fluctuations of the inactive structure alone for allosteric proteins in the Ras and other Ras-like families, our method identified allosterically important residues not only as strongly coupled ones but also in densely connected regions of the interaction graph formed by the inferred couplings. Our results were in good agreement with other empirical findings. By studying distinct members of the Ras, Rho and Rab sub-families, we show further that KPCCA was capable of inferring common allosteric characteristics in the small G protein super-family.Availability and implementation: https://github.com/lsgh/ismb15Contact: lsoltang@uwaterloo.ca

Statistical downscaling model for late-winter rainfall over Southwest China

A statistical downscaling model is built for the late-winter rainfall over Southwest China (SWC). A partial-correlation method is used for selecting factors. The results show that the selected factors for late-winter rainfall in SWC are sea level pressure in Western Europe (SNAO) and sea surface temperature in Western Pacific (WPT). SNAO is related to the southern pole of North Atlantic Oscillation (NAO) and excites Southern Eurasian teleconnection, which influences the development of the southern branch trough and the water vapor transport to SWC. WPT indicates the variability of ENSO in the tropical Western Pacific. WPT excites Pacific-East Asia teleconnection and an anticyclone (cyclone) is formed in the southern part of China and suppresses (enhances) rainfall over SWC. A regression statistical downscaling model using SNAO and WPT shows good performance in fitting the variability of late-winter rainfall in the whole SWC region and every observation station, and the model also shows strong robustness in the independent validation. The statistical model can be used for downscaling output from seasonal forecast numerical models and improve the SWC late winter rainfall prediction in the future.

Notable shifting in the responses of vegetation activity to climate change in China

The weakening relationship between inter-annual temperature variability and vegetation activity in the Northern Hemisphere over the last three decades has been reported by a recent study. However, how and to what extent vegetation activity responds to climate change in China is still unclear. We applied the Pearson correlation and partial correlation methods with a moving 15-y window to the GIMMS NDVI dataset from NOAA/AVHRR and observed climate data to examine the variation in the relationships between vegetation activity and climate variables. Results showed that there was an expanding negative response of vegetation growth to climate warming and a positive role of precipitation. The change patterns between NDVI and climate variables over vegetation types during the past three decades pointed an expending negative correlation between NDVI and temperature and a positive role of precipitation over most of the vegetation types (meadow, grassland, shrub, desert, cropland, and forest). Specifically, correlation between NDVI and temperature (PNDVI-T) have shifted from positive to negative in most of the station of temperature-limited areas with evergreen broadleaf forests, whereas precipitation-limited temperate grassland and desert were characterized by a positive PNDVI-P. This study contributes to ongoing investigations of the effects of climate change on vegetation activity. It is also of great importance for designing forest management strategies to cope with climate change.

Economic development and construction productivity in Malaysia

The productivity of the construction industry has a significant effect on national economic growth. Gains from higher construction productivity flow through the economy, as all industries rely on construction to some extent as part of their business investment. Contractions and expansions of economic activity are common phenomena in an economy. Three construction cycles occurred between the years 1970 and 2011 in Malaysia. The relationships between construction productivity and economic development are examined by the partial correlation method to establish the underlying factors driving the change in construction productivity. Construction productivity is statistically significantly correlated with gross domestic product (GDP) per capita in a positive direction for the 1985–98 and 1998–2009 cycles, but not the 1970–85 cycle. Fluctuations in construction activities and the influx of foreign workers underlie the changes of construction productivity in the 1985–98 cycle. There was less fluctuation in construction activities in the 1998–2009 cycle, with changes being mainly due to the fiscal stimulation policies of the government in attempting to stabilize the economy. The intensive construction of mega-projects resulted in resource constraints and cost pressures during the 1980s and 1990s. A better management of the ‘boom-bust’ nature of the construction business cycle is required to maintain the capability and capacity of the industry.

Partial correlation analysis for functional connectivity studies in cortical networks

The use of in vitro neuronal systems and computational models has strongly contributed to the better understanding of relevant neurophysiological principles. Taking advantages form these approaches, here we propose a revised method of partial correlation analysis to investigate functional connectivity in neuronal networks. The main goal of this work is to estimate the functional connectivity, by means of correlation and information theory-based methods, from the spontaneous activity of dissociated cortical neurons developing in vitro and coupled to micro-electrode arrays (MEAs). In particular we focused here on the Partial Correlation (PC) method [1] compared to Transfer Entropy [2] and Cross Correlation (CC) [3] algorithms. We first evaluated the methods’ performances applying the algorithms to a neural network model made up of 60 spatially distributed and synaptically connected Izhikevich neurons [4]. Using receiver operating characteristic (ROC) curves, calculating the values of the areas under these curves (AUC) and varying the average degree of each cell, we observed that Partial Correlation presented the best performances (see Figure ​Figure1)1) for all the tested average synaptic connectivity degree (from 5 to 55 connections for each neuron). Figure 1 AUC values: Comparison among the three algorithms (PC, CC and TE). Second we assessed the statistical significance of connections extracted through the aforementioned algorithms from electrophysiological data. Using “shuffling” techniques devised in [5], we implemented a reliable threshold-independent test, model free and not linked to any particular initial assumptions (i.e., choice of data distribution). Finally, applying the validated methods, we obtained functional mapping of biological in vitro models of cortical networks and we extracted relevant topological features.

Resting-state functional connectivity abnormalities in first-onset unmedicated depression.

Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We collected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Automated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We selected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effective feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more significant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.

Impacts of Different Types of El Niño on the East Asian Climate: Focus on ENSO Cycles

Using multiple datasets and a partial correlation method, the authors analyze the different impacts of eastern Pacific (EP) and central Pacific (CP) El Niño on East Asian climate, focusing on the features from El Niño developing summer to El Niño decaying summer. Unlike the positive–negative–positive (+/−/+) anomalous precipitation pattern over East Asia and the equatorial Pacific during EP El Niño, an anomalous −/+/− rainfall pattern appears during CP El Niño. The anomalous dry conditions over southeastern China and the northwestern Pacific during CP El Niño seem to result from the anomalous low-level anticyclone over southern China and the South China Sea, which is located more westward than the Philippine Sea anticyclone during EP El Niño. The continuous anomalous sinking motion over southeastern China, as part of the anomalous Walker circulation associated with CP El Niño, also contributes to these dry conditions. During the developing summer, the impact of CP El Niño on East Asian climate is more significant than the influence of EP El Niño. During the decaying summer, however, EP El Niño exerts a stronger influence on East Asia, probably due to the long-lasting anomalous warming over the tropical Indian Ocean accompanying EP El Niño. Temperatures over portions of East Asia and the northwestern Pacific tend to be above normal during EP El Niño but below normal from the developing autumn to the next spring during CP El Niño. A possible reason is the weakened (enhanced) East Asian winter monsoon related to EP (CP) El Niño.

Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data.

Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared the outputs of two methods using connectivity as a criterion, as it measures how well a node (gene) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to phenotypes. We observed that, in contrast to WGCNA method, PCIT algorithm removes many of the edges of the most highly interconnected nodes. Removal of edges of most highly connected nodes or hub genes will have consequences for downstream analyses and biological interpretations. In general, for large GCN construction (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended.

The Effect of Aging and Severity of Sleep Apnea on Heart Rate Variability Indices in Obstructive Sleep Apnea Syndrome

ObjectiveThis study aims to analyze how much heart rate variability (HRV) indices discriminatively respond to age and severity of sleep apnea in the obstructive sleep apnea syndrome (OSAS).Methods176 male OSAS patients were classified into four groups according to their age and apnea-hypopnea index (AHI). The HRV indices were compared via analysis of covariance (ANCOVA). In particular, the partial correlation method was performed to identify the most statistically significant HRV indices in the time and frequency domains. Stepwise multiple linear regressions were further executed to examine the effects of age, AHI, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and sleep parameters on the significant HRV indices.ResultsThe partial correlation analysis yielded the NN50 count (defined as the number of adjacent R-wave to R-wave intervals differing by more than 50 ms) and low frequency/high frequency (LF/HF) ratio to be two most statistically significant HRV indices in both time and frequency domains. The two indices showed significant differences between the groups. The NN50 count was affected by age (p<0.001) and DBP (p=0.039), while the LF/HF ratio was affected by AHI (p<0.001), the amount of Stage 2 sleep (p=0.005), and age (p=0.021) in the order named in the regression analysis.ConclusionThe NN50 count more sensitively responded to age than to AHI, suggesting that the index is mainly associated with an age-related parasympathetic system. On the contrary, the LF/HF ratio responded to AHI more sensitively than to age, suggesting that it is mainly associated with a sympathetic tone likely reflecting the severity of sleep apnea.